Great GTM Starts With Weird Problems ft. CheeTung Leong

Great GTM Starts With Weird Problems ft. CheeTung Leong

In today's episode, I chat with CheeTung Leong, founder at The GTM Architects, about helping mid-size B2B software and services companies build genuinely AI-native go-to-market systems—not just bolting AI onto the same old ZoomInfo and Outreach pipeline.  The campaigns he walks through are real problem-solving exercises: finding car washes and narrowing a massive TAM down by digging into public records for how much each one actually spends on water, and building first-party signal systems for a 100K-user client by pulling together HubSpot, Postgres, and product analytics to define custom lead states and the next best action for each one. CheeTung's background is 10 years as a tech founder—enterprise consulting selling to Fortune 500s, a venture-funded startup raised to Series A and $3M ARR with a 14-person GTM team, a bootstrapped SaaS attempt, and eventually building The GTM Architects after deciding not to push a dying edtech business uphill. He's now running 70% of his work through Claude Code, building a skill that pulls from nine or ten sources—DKIM/DMARC settings, website HTML stack detection, social posts—into an Opus-strategist agent that outputs full pre-call research documents, and he's publishing fully-sourced 3,700-word blog teardowns weekly using the same system, work that used to take a month per article. His prediction: the bleeding-edge GTM engineers will burn out as starving artists while tools like Clay move upmarket toward enterprises who'd rather hire average operators than chase geniuses—but the fundamentals of people-to-people selling won't change, just the speed at which the tooling evolves. His advice: take on early clients for free to build proof, then charge, and remember the build itself is commoditized now—your own thinking is the only thing that isn't.  Enjoy 🙂 (0:00) Introduction to The GTM Engineer Podcast  (0:27) What The GTM Architects Does: AI-Native GTM Systems for Mid-Size B2B Companies  (2:06) The Car Wash Campaign: Narrowing TAM by Digging Into Public Water Spend Records  (3:05) First-Party Signal Systems: Combining HubSpot, Postgres, and Product Analytics for 100K Users  (7:08) CheeTung's Journey: Enterprise Consulting, a Series A Startup, and Shutting Down to Build GTM Architects  (9:06) Running 70% of Work Through Claude Code: The Pre-Call Research Skill Across 10 Sources  (11:46) Publishing Fully-Sourced 3,700-Word GTM Teardowns Weekly With Fable 5  (14:03) Predictions: The Starving Artist GTME, Clay Moving Upmarket, and Why the Fundamentals Won't Change  (19:22) Advice: Take Early Clients for Free, Then Charge—The Build Is Commoditized, Your Thinking Isn't 🔗 CONNECT WITH CHEETUNG 👥 LinkedIn [https://www.linkedin.com/in/cheetung/]  💻 Website [https://www.thegtmarchitects.com]  🔗 CONNECT WITH SAURAV 🎥 YouTube Channel [https://www.youtube.com/@_sauravgupta/videos] 🐦 X (Twitter) [https://x.com/saguppa] 📸 Instagram [https://www.instagram.com/saurav_salesrobot] 💻 Website [https://www.salesrobot.co/] 👥 LinkedIn [https://www.linkedin.com/in/saurav-g-43b959225]📧 Email - saurabh@salesrobot.co 🙏 LEAVE A REVIEW If you enjoyed listening to The GTM Engineer Podcast, we'd love for you to leave a 5-star review on Apple Podcasts to help others discover the show :) 👋🏼 GET IN TOUCH You can also reach out with any feedback, ideas or thoughts about the lessons you've learned from the episodes.

Agencies Need Retention Systems, Not Better Outreach ft. Brian Attarbashi

In today's episode, I chat with Brian Attarbashi, founder at AI Bees and AI Ark, about what it actually takes to run a 50-person outbound agency across 17 countries while simultaneously building a data tool designed to fix the exact problems you keep running into as an agency.  AI Bees delivers what Brian calls "meet me leads"—qualified replies handed to an AI that takes the conversation from interested to booked meeting—across cold email, LinkedIn outreach, and a content-plus-engagement layer where AI jumps in on every like, comment, or share to start a conversation. AI Ark came out of watching agencies hammer outdated Apollo scraper data and never knowing which client a contact was used for—so they built fresh 30-day-max data with keyword search on profiles, eating their own dog food with every campaign. The most honest part of this episode is Brian's breakdown of churn: clients leave even when you deliver perfect results, because the sales team doesn't close, the product isn't ready, or they just run out of budget—so AI Bees built a churn risk score from bi-weekly account manager calls, an AI that scores the conversation 1-10 and triggers a recovery plan when it gets high. His path started in 2018 with his brother, discovered the math of recurring revenue through a white-label LinkedIn tool, hit a million in 18 months, and put all the profits into building AI Ark. His advice: master data hygiene and inbox delivery before touching tools, and stay on top of the game through YouTube University.  Enjoy 🙂 (0:00) Introduction to The GTM Engineer Podcast  (0:22) What AI Bees Does: Meet Me Leads, LinkedIn Content, and AI-Powered Conversation Nurture  (2:39) What AI Ark Does: 30-Day-Fresh Data and Keyword Search on Profiles  (4:18) Why Brian Built AI Ark: Outdated Scraper Data and No Campaign Attribution for Agencies  (7:01) The Real Talk on Churn: Clients Leave Even When You Do a Perfect Job  (9:10) The Churn System: AI Risk Scores on Account Manager Calls, Recovery Plans, and Review Psychology  (13:04) Brian's Journey: White-Label LinkedIn Tool, the Million-Dollar Math, and Reinvesting Into AI Ark  (15:38) Predictions: Personalization Becomes the Norm, Signal Data Grows in Importance, LinkedIn Still Has an Edge  (19:04) Advice: Master the Basics First, Then Stay on Top via YouTube University 🔗 CONNECT WITH BRIAN 👥 LinkedIn [https://www.linkedin.com/in/brian-h-attarbashi/]  💻 Website [https://ai-ark.com]  🔗 CONNECT WITH SAURAV 🎥 YouTube Channel [https://www.youtube.com/@_sauravgupta/videos] 🐦 X (Twitter) [https://x.com/saguppa] 📸 Instagram [https://www.instagram.com/saurav_salesrobot] 💻 Website [https://www.salesrobot.co/] 👥 LinkedIn [https://www.linkedin.com/in/saurav-g-43b959225]📧 Email - saurabh@salesrobot.co 🙏 LEAVE A REVIEW If you enjoyed listening to The GTM Engineer Podcast, we'd love for you to leave a 5-star review on Apple Podcasts to help others discover the show :) 👋🏼 GET IN TOUCH You can also reach out with any feedback, ideas or thoughts about the lessons you've learned from the episodes.

Learn Sales Before You Learn Clay ft. Sashank Koundinya

In today's episode, I chat with Sashank, GTM engineer at Interact, about what it looks like to run 15 simultaneous demand-generation engines for a category-creation product—where the job isn't capturing existing demand but making people care about a problem they don't yet know they have.  The standout campaign is a segmentation approach that buckets prospects by how urgently the product solves their specific problem, reconstructing their HubSpot in the background from LinkedIn data to simulate their customer base—producing 35-40% reply rates and cutting average sales cycles from 45-50 days down to 18-20. Alongside email and LinkedIn, Sashank is running gifting campaigns, field marketing with Hallmark cards that link directly to the AI agent they're selling, and a "show don't tell" philosophy where every campaign ends with the prospect actually talking to the product. His path is one of the wildest on the show: professional musician, artist manager at 17 with an eight-figure exit he donated, keyframe animation studio founder, pre-sales engineer, SDR, CSM at Factors where he built 200+ automations in 14 months, and now sitting on $1.6M in pipeline solo at Interact within one month. His prediction: the "I'll build it myself" wave will die when people discover the maintenance reality—software ages like fish, not frozen meat. His advice: GTM engineering is an evolution, not an escape from sales—understand GTM deeply before touching the engineering side.  Enjoy 🙂 (0:00) Introduction to The GTM Engineer Podcast  (0:21) What Sashank Does: 15 Demand-Gen Engines for a Category-Creation Product at Interact  (1:40) The Psychological Bucketing Campaign: Simulating Prospect HubSpots, 35-40% Reply Rates  (4:39) Show Don't Tell: Every Campaign Ends With the Prospect Talking to the Agent  (6:43) Field Marketing, Gifting, and Hallmark Cards That Link Directly to the Product  (7:51) Sashank's Journey: Musician to Animation Studio to SDR to CSM to GTM Engineer  (12:00) 1,500 Applications, 392 Interviews, 18 Offers—and $1.6M in Pipeline in One Month  (15:49) Predictions: The Build-It-Yourself Wave Will Die When Maintenance Reality Hits  (19:27) Advice: GTM Engineering Is an Evolution, Not an Escape—Understand GTM Before the Engineering 🔗 CONNECT WITH SASHANK 👥 LinkedIn [https://www.linkedin.com/in/sashank-koundinya/]  🔗 CONNECT WITH SAURAV 🎥 YouTube Channel [https://www.youtube.com/@_sauravgupta/videos] 🐦 X (Twitter) [https://x.com/saguppa] 📸 Instagram [https://www.instagram.com/saurav_salesrobot] 💻 Website [https://www.salesrobot.co/] 👥 LinkedIn [https://www.linkedin.com/in/saurav-g-43b959225]📧 Email - saurabh@salesrobot.co 🙏 LEAVE A REVIEW If you enjoyed listening to The GTM Engineer Podcast, we'd love for you to leave a 5-star review on Apple Podcasts to help others discover the show :) 👋🏼 GET IN TOUCH You can also reach out with any feedback, ideas or thoughts about the lessons you've learned from the episodes.

AI Makes Customer Research More Important ft. Maitri Gandhi

In today's episode, I chat with Maitri Gandhi, product marketer at Hoshizaki America, about what GTM looks like in one of the most traditional B2B industries out there—commercial ice machines and refrigeration.  Hoshizaki sells through a layered channel model spanning dealers, distributors, end users, and consultants, each with completely different priorities, which means Maitri is managing multiple ICPs and messaging frameworks simultaneously rather than one clean persona. She walks through how trade shows are still the primary outreach channel, how sales enablement—brochures, one-pagers, decks, trainings—is where most of her creativity goes, and how ice itself is a surprisingly rich messaging challenge: eight different ice types, each with different density and dilution properties, and a key angle around replacing expensive packed ice with an on-site machine that pays for itself over time. Her path ran from fashion design at NIF in India to fashion marketing in the US to content marketing in fintech and agency roles, until she started asking "who's reading this and why" about every blog she wrote—which led naturally into product marketing. Her prediction: AI is helping sales teams narrow to the right ICP faster and enrichment tools like Clay are solving the data accuracy problem, which frees everyone to focus on what actually matters—the messaging. Her advice: know your ICP before you know your product, because real understanding only comes from actual conversations, not sitting at a desk.  Enjoy 🙂 (0:00) Introduction to The GTM Engineer Podcast  (0:22) What Maitri Does: Product Marketing at Hoshizaki Across Ice Machines and Refrigeration  (0:35) The Layered Channel Model: Dealers, Distributors, End Users, and Consultants — All Different ICPs  (3:52) Sales Enablement as the Creative Engine in a Relationship-Driven Industry  (5:28) Ice as a Messaging Challenge: Eight Types, Density, Dilution, and the Packed Ice Angle  (7:01) Maitri's Journey: Fashion Design in India to Content Marketing to Product Marketing  (10:14) Predictions: AI Narrows ICP Faster, Enrichment Solves Data Accuracy, Messaging Becomes the Focus  (14:18) Advice: Know Your ICP Before You Know Your Product — Real Understanding Comes From Conversations 🔗 CONNECT WITH MAITRI 👥 LinkedIn [https://www.linkedin.com/in/maitrigandhi00/]  🔗 CONNECT WITH SAURAV 🎥 YouTube Channel [https://www.youtube.com/@_sauravgupta/videos] 🐦 X (Twitter) [https://x.com/saguppa] 📸 Instagram [https://www.instagram.com/saurav_salesrobot] 💻 Website [https://www.salesrobot.co/] 👥 LinkedIn [https://www.linkedin.com/in/saurav-g-43b959225]📧 Email - saurabh@salesrobot.co 🙏 LEAVE A REVIEW If you enjoyed listening to The GTM Engineer Podcast, we'd love for you to leave a 5-star review on Apple Podcasts to help others discover the show :) 👋🏼 GET IN TOUCH You can also reach out with any feedback, ideas or thoughts about the lessons you've learned from the episodes.

The Future Of Sales Is More Human ft. Tom Dingwall

In today's episode, I chat with Tom Dingwall, AI Growth Manager at Passionfruit, about why the future of GTM engineering is human connection enabled by AI — not AI replacing humans.  Passionfruit is a freelance marketplace helping companies flexibly resource their teams, with an AI tool (PIP AI) built on top to help marketers free up time from repetitive work and focus on real relationships. Tom's role sits somewhere between SDR and AE — he runs outbound, takes calls, and works deals through to close — and he walks through what that looks like day to day, including how Passionfruit's cold calling unit of four optimizes not just on list quality but on what they're saying in the first few seconds and when they're calling. He makes a strong case for AI as a calendar-clearing tool rather than a headcount-cutting one: use it to find the right companies, research why they're a fit, and then show up to the call with nothing to do but actually connect. We also get into his path — PPE at uni, six months in recruitment, then a co-founder at Passionfruit reached out — and why he ended up in sales specifically because of how human the job is. His predictions for the next few years are clear: companies will split into those that overshoot on AI and lose the human side, those that ignore it and fall behind, and the ones that get the balance right and compound hard. He closes with two pieces of advice: if you don't genuinely enjoy talking to people all day, GTM isn't for you — and if you do, go deep on AI tools, because the people who combine both will be world-class. Enjoy 🙂 (0:00) Introduction to The GTM Engineer Podcast  (0:21) Tom's Role as AI Growth Manager at Passionfruit: SDR, AE, and Everything Between  (1:29) Creative Campaigns and Cold Calling Optimization at Passionfruit  (3:59) How Tom Uses AI to Build Lists — and When He Steps Away From It  (5:18) Why Teams Enabling SDRs With AI Beat Teams Replacing Them  (7:31) Tom's Journey: PPE, Recruitment, and Landing at Passionfruit  (9:49) Predictions: The Three Types of Sales Teams That Will Emerge in the AI Era  (13:35) Advice for Aspiring GTM Engineers: Grit, People Skills, and AI Literacy 🔗 CONNECT WITH TOM 👥 LinkedIn [https://www.linkedin.com/in/thomas-dingwall/]  💻 Website [https://www.linkedin.com/company/usepassionfruit/]  🔗 CONNECT WITH SAURAV 🎥 YouTube Channel [https://www.youtube.com/@_sauravgupta/videos] 🐦 X (Twitter) [https://x.com/saguppa] 📸 Instagram [https://www.instagram.com/saurav_salesrobot] 💻 Website [https://www.salesrobot.co/] 👥 LinkedIn [https://www.linkedin.com/in/saurav-g-43b959225]📧 Email - saurabh@salesrobot.co 🙏 LEAVE A REVIEW If you enjoyed listening to The GTM Engineer Podcast, we'd love for you to leave a 5-star review on Apple Podcasts to help others discover the show :) 👋🏼 GET IN TOUCH You can also reach out with any feedback, ideas or thoughts about the lessons you've learned from the episodes.